The demand for reliable and high data throughput wireless communication networks has never been so great as in the present. While initial consumer and business demand was for wireless communication technologies to support voice communication, this demand has grown both in terms of the sheer volume of users as well as the bandwidth requirements; the latter being the result of demand for wireless broadband data services. These services are provided, for example, by Fourth Generation (“4G”) wireless systems based on 3GPP Long Term Evolution (“LTE”), IEEE 802.16e WiMax, and 3GPP2 Ultra Mobile Broadband (UMB), each of which use orthogonal frequency division multiple access (“OFDMA”) technology as the air interface technology. These 4G systems are mandated to support mobility of up to 350 km/hr and such systems can be deployed in various channel frequency bands up to 5 GHz.
OFDMA is a modulation and multiple wireless network access scheme in which large channel bandwidth is divided into numerous orthogonal narrowband subcarriers. Information data symbols are modulated onto these subcarriers. Because the subcarriers are narrowband, the OFDMA symbols have longer time durations. OFDMA symbols are relatively immune to multi-path and inter-symbol interferences from the radio (wireless network) channel as a result of long symbol durations.
In practical use, symbols transmitted using OFDMA propagate through a wireless fading channel which distorts the transmitted signal amplitude and distorts the phase. On the other hand, channel induced amplitude and phase needs to be estimated at the receiver in order to correctly demodulate the information symbols from the OFDMA subcarriers. Channel estimation is therefore an important aspect of receiver functionality. To aid channel estimation in OFDMA systems, some of the subcarriers in the OFDMA symbol are reserved for use as pilots. Pilot subcarriers carry symbols which are apriori known to the receiver, i.e., not user data information symbols.
OFDMA channel estimation is normally a two-step procedure. The first step is to estimate the channel for the pilot subcarriers that carry known symbols. The second step is to interpolate the channel estimate for information data bearing subcarriers using the channel estimates from pilot subcarriers. There are several known techniques for performing such interpolation, e.g., linear, second order, spline, interpolation with low pass filtering, etc.
It stands to reason that, for the interpolated channel estimates to be accurate, the estimates obtained from pilot subcarriers have to be very accurate. Therefore, the first step of extracting accurate pilot subcarrier channel estimates in presence of channel impairments and interference is an important step. A major source of interference in OFDMA systems is known as inter carrier interference (“ICI”). ICI causes subcarriers to interfere with one another resulting in degradation of the signal to noise ratio (“SNR”). ICI can occur due to the spectral broadening of the subcarriers due to Doppler offset and Doppler spread due to mobility of the receiving mobile terminals. Doppler shift/spread in the received frequency can be written as:
                              f          d                =                                            f              c                        ⁢            v                    c                                    (        1        )            
In equation (1), fd is the Doppler shift/spread in received frequency, fc is the channel transmit/receive frequency, v is the velocity of the receive terminal, and c is the velocity of light. As the Doppler shift/spread grows to more than 1% of the subcarrier spacing, the ICI degrades the pilot SNR resulting in inaccurate pilot channel estimates and the resultant interpolated channel estimates. Unreliable channel estimation at high ICI levels causes the demodulated data error probability to increase significantly. For example, it has been found that there is approximately a 15 dB degradation in SNR at a bit error rate (“BER”) of 0.01 when Doppler shift is 10% of the subcarrier spacing.
As noted above, 4G wireless communication technologies include LTE, WiMAX and UMB. The subcarrier spacings are 15 KHz, 10.94 KHz, and 9.6 KHz for LTE, WiMax and UMB respectively. For a 5 GHz channel frequency, at mobility of 350 km/hr, Doppler shifts are 10.8%, 14.81%, and 16.88% of subcarrier spacing for LTE, WiMax, and UMB based wireless systems, respectively. The result is that more than a 15 dB degradation in SNR vs. BER performance is expected at a 5 GHz channel frequency at a mobility speed of 350 km/hr when conventional channel estimation methods are employed in the receiver. In other words, ICI increases when the Doppler shill increases because the sub-carriers are spread out and begin to interfere with one another.
Channel estimation techniques try to estimate the channel so that the mean square error between the actual channel and the estimated channel is minimized. Filtering based techniques are simple to implement, but need optimization of the bandwidth of the filter which is very difficult to accomplish when the operating environment results in changing mobile terminal mobility, e.g., speed, conditions.
Minimum mean squared error (“MMSE”) estimators have also been used. MMSE techniques utilize the second-order statistics of the channel conditions to minimize the mean-square error of the channel estimates. Performance using MMSE estimators is good at low mobility, but degrades at high mobility. The major drawbacks of MMSE estimators are (i) the need to know second order moments of the channel, and (ii) high computational complexity, especially if matrix inversions are needed each time the data changes. The result is that MMSE estimators are not suitable for deployment within mobile terminals.
Methods based on least squares (“LS”) techniques have also been tried. LS techniques do not require any knowledge of the statistics of the channels. However, because LS estimators use calculations with very low complexity, they suffer from a high mean-square error, especially under low SNR conditions.
It is therefore desirable to have a system and method that can more accurately estimate the wireless communication channel conditions and that is deployable in a wireless mobile device. Such estimation should be suitable for deployment in wireless broadband communication systems where ICI is a factor.